有交易成本的模糊最优化投资

本文针对交易成本在证券组合投资中的重要地位 ,提出了考虑交易成本 ,并兼顾收益与风险的模糊最优化投资模型 ,分析了交易成本对投资有效边界的影响 ,并给出了最优投资比例公式 .这对投资者进行投资有重要的理论与实践意义 .最后 ,通过释例进行了说明 .     

带有交易成本和红利的最优消费投资策略研究

本文利用HJB方程粘性解理论,考虑带有红利收益和交易成本后,对现有最优消费投资模型作了推广,研究了投资者在带有红利和交易成本情形下的最优消费投资策略。     

共同资金投资组合的有效边界与最优策略

本文利用均值方差模型,分析了非线性交易成本下的共同资金投资的有效边界和在一般的效用函数下讨论了最优投资组合和最大效用,其中只考虑风险资产的总投资比例对交易成本的影响.     

具有交易成本的证券组合投资决策研究

本文利用均值－方差模型，分析了有交易成本的证券投资组合的决策问题，给出了风险资产和无风险资产的最优投资比例与交易成本关系的一个有意义的结论。     

具有熵约束的多阶段均值—绝对偏差模糊投资组合决策

文章运用可能性绝对偏差和比例熵分别度量风险和分散化程度,提出了具有风险控制和线性交易成本的终期财富最大化的多阶段模糊投资组合模型。运用可能理论,将该模型转化为显示的非线性动态优化问题。由于投资过程存在交易成本,上述模型为具有路径依赖性的动态优化问题。文章提出了前向动态规划方法求解。最后, 通过实证研究比较了不同熵的取值投资组合最优投资比例和最终财富的变化。     

含有资本结构因子、交易成本和风险偏好的模糊最优化投资模型

建立了含有资本结构因子、交易成本和风险偏好的模糊最优化投资模型,在允许卖空条件下,给出最优投资策略及有效边界;在不允许卖空条件下,给出了确定其有效边界的算法,并分析了风险偏好、无风险利率和交易成本对有效边界的影响,最后通过示例进行了分析.     

有交易成本的回望期权定价研究

基于标的资产价格的几何布朗运动假设，Black—Seholes模型运用连续交易保值策略成功解决了完全市场下的欧式期权定价问题。然而，在实际的金融市场中，存在着数量可观的交易成本。本文主要研究了在不完全市场下有交易成本的回望期权的定价问题，并且利用Ito公式，得到了在该模型下期权价格所满足的微分方程。     

具有实际约束的多阶段M-V投资组合时间一致性策略研究

从动态规划的角度分析,方差算子的不可分离性导致标准的多阶段均值-方差模型的最优投资策略不满足时间一致性。文章采用条件期望映射的方法,构建了一个具有交易成本、借贷约束和阈值约束的多阶段M-V投资组合模型。由于考虑了交易成本,该模型是一个具有路径依赖性的动态优化问题。为了获得其时间一致性投资策略,文章将该问题近似地转化为连续性动态规划模型,证明最优解的近似度,并运用离散迭代算法求解。最后,使用上海证券交易所的部分历史数据验证了模型和算法的有效性。     

分形布朗运动下有交易成本的外汇期权定价

研究了有交易成本的分形Black-Scholes外汇期权定价问题.基于汇率的分形布朗运动分布假设,运用分形布朗运动的性质和随机微积分方法,得到了欧式外汇期权价格所满足的偏微分方程.最后,建立离散时间条件下的非线性期权定价模型,并且通过解期权价格的偏微分方程给出了有交易成本的欧式外汇期权定价公式.     

含有交易成本的均值-方差-偏度资产组合优化模型

提出了含有交易成本的均值 -方差 -偏度资产组合优化模型 ;结合一个非对称性收益分布的具体例子 ,对模型做了灵敏度分析 .     

一类灰色组合投资决策方法

以灰色系统理论和概率论为基础,探讨了含有区间灰数的组合投资决策问题,提出了具有交易费用的灰色组合投资模型的有效解及其临界最优解和均值白化最优解的概念.并且指出了这些概念所对应的投资偏好.利用分析方法和技巧,融合经典组合投资理论,构建了带有交易费用的灰色组合投资模型的熵权分析算法.为不确定型组合投资决策方法的研究提出了一条新思路.文中的算例说明了算法的可行性.     

Selecting Portfolios with Fixed Costs and Minimum Transaction Lots

The original Markowitz model of portfolio selection has received a widespread theoretical acceptance and it has been the basis for various portfolio selection techniques. Nevertheless, this normative model has found relatively little application in practice when some additional features, such as fixed costs and minimum transaction lots, are relevant in the portfolio selection problem. In this paper different mixed-integer linear programming models dealing with fixed costs and possibly minimum lots are introduced. Due to the high computational complexity of the models, heuristic procedures, based on the construction and optimal solution of mixed integer subproblems, are proposed. Computational results obtained using data from the Milan Stock Exchange show how the proposed heuristics yield very good solutions in a short computational time and make possible some interesting financial conclusions on the impact of fixed costs and minimum lots on portfolio composition.     

Mean-variance portfolio rebalancing with transaction costs and funding changes

Investment portfolios should be rebalanced to take account of changing market conditions and changes in funding. Standard mean-variance (MV) portfolio selection methods are not appropriate for portfolio rebalancing, as the initial portfolio, change in funding and transaction costs are not considered. A quadratic mixed integer programming portfolio rebalancing model, which takes account of these factors is developed in this paper. The transaction costs in this portfolio rebalancing model are composed of fixed charges and variable costs, including the market impact costs associated with large market trades of individual securities, where these variable transaction costs are assumed to be non-linear functions of traded value. The use of this model is demonstrated and it is shown that when initial portfolio, funding changes and transaction costs are taken into account in portfolio construction and rebalancing, MV efficient portfolios that include risk-free lending do not have the structure expected from portfolio theory.     

Portfolio optimization with linear and fixed transaction costs

We consider the problem of portfolio selection, with transaction costs and constraints on exposure to risk. Linear transaction costs, bounds on the variance of the return, and bounds on different shortfall probabilities are efficiently handled by convex optimization methods. For such problems, the globally optimal portfolio can be computed very rapidly. Portfolio optimization problems with transaction costs that include a fixed fee, or discount breakpoints, cannot be directly solved by convex optimization. We describe a relaxation method which yields an easily computable upper bound via convex optimization. We also describe a heuristic method for finding a suboptimal portfolio, which is based on solving a small number of convex optimization problems (and hence can be done efficiently). Thus, we produce a suboptimal solution, and also an upper bound on the optimal solution. Numerical experiments suggest that for practical problems the gap between the two is small, even for large problems involving hundreds of assets. The same approach can be used for related problems, such as that of tracking an index with a portfolio consisting of a small number of assets.     

Intertemporal portfolio optimization with small transaction costs and stochastic variance

The solution to the intertemporal optimal portfolio selection and consumption rule with small transaction costs is derived via the use of perturbation analysis for the two assets portfolio, one risky and one riskfree. This methodology allows us to apply a broader specification for the function of utility. The additional feature of stochastic variance is also included.     

手数约束和凹交易费下的离散投资组合模型及算法

本文建立带手数约束和凹交易费的离散投资组合模型,给出求解该模型的一种精确算法。该算法是一个基于拉格朗日松弛和次梯度对偶搜索的分枝定界算法。为测试算法的有效性,用随机产生的数据对模型进行数值实验。作为其应用,用沪深300指数的真实数据实证检验该模型,并与不含交易费用的离散投资组合模型进行数值比较分析。数值分析表明算法能在合理的时间内给出模型的投资组合策略, 对解决中小规模的离散投资组合问题是有效的。     

Perturbation solution of optimal portfolio theory with transaction costs for any utility function

The solution to the optimal portfolio selection and consumptionrule with small transaction costs is derived via the use ofperturbation analysis for the case when one risky and one risklessasset are available for investment. This methodology allowsus to apply a broader specification for the utility function.     

基于直觉模糊规划的多目标投资组合选择模型

将直觉模糊集合的概念引入投资组合模型中,并将多目标投资组合模型中的收益、方差和偏度三个目标模糊化,用隶属函数与非隶属函数作为新的目标函数.针对该模糊多目标投资组合模型,提出了一个动态遗传算法,算例给出了该模型的一个实例的最优解.     

均值—动态VaR多阶段投资组合优化研究

将动态风险度量方法运用到多阶段投资组合中,提出了具有交易成本和交易量限制的均值—动态VaR多阶段投资组合模型,并运用自创算法——离散近似迭代法求解.方法的基本思路为:首先,将模型中的连续型状态变量离散化,并将上述模型转化多阶段赋权有向图,然后,运用极大代数求出起点至终点的最长路程,即获得模型的一个可行解;最后,以该可行解为基础,继续迭代直到前后两个可行解非常接近.证明了该方法的收敛性,并以一个具体的算例,验证了该算法可以较快地计算出不同终期财富所对应的最优投资策略.